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The Organization of Behavior
Abstract
Since its publication in 1949, D.O. Hebb's, The Organization of Behavior has been one of the most influential books in the fields of psychology and neuroscience. However, the original edition has been unavailable since 1966, ensuring that Hebb's comment that a classic normally means "cited but not read" is true in his case. This new edition rectifies a long-standing problem for behavioral neuroscientists--the inability to obtain one of the most cited publications in the field. The Organization of Behavior played a significant part in stimulating the investigation of the neural foundations of behavior and continues to be inspiring because it provides a general framework for relating behavior to synaptic organization through the dynamics of neural networks. D.O. Hebb was also the first to examine the mechanisms by which environment and experience can influence brain structure and function, and his ideas formed the basis for work on enriched environments as stimulants for behavioral development. References to Hebb, the Hebbian cell assembly, the Hebb synapse, and the Hebb rule increase each year. These forceful ideas of 1949 are now applied in engineering, robotics, and computer science, as well as neurophysiology, neuroscience, and psychology--a tribute to Hebb's foresight in developing a foundational neuropsychological theory of the organization of behavior.
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“In biological systems, experiences that are repeatedly co-activated gradually develop stronger associations, a phenomenon often summarized as 'neurons that fire together wire together' (illustrated in Figure 2).”
From HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents (2026) · cites this paper as Hebb, 2005
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Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
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Kohonen, T. (1982) · Biological Cybernetics · doi:10.1007/bf00337288 · Cited by 9,529
An introduction to computing with neural nets
Lippmann, R. P. (1987) · IEEE ASSP Magazine · doi:10.1109/massp.1987.1165576 · Cited by 7,890
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McClelland, J. L., McNaughton, B. L., & O'Reilly, R. C. (1995) · Psychological Review · doi:10.1037/0033-295x.102.3.419 · Cited by 5,256
Working memory: Looking back and looking forward
Baddeley, A. (2003) · Nature Reviews Neuroscience · doi:10.1038/nrn1201 · Cited by 5,200
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Jain, A. K., Mao, J., & Mohiuddin, K. M. (1996) · Computer · doi:10.1109/2.485891 · Cited by 5,095
Bi, G.-Q., & Poo, M.-M. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-24-10464.1998 · Cited by 4,744
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McGaugh, J. L. (2000) · Science · doi:10.1126/science.287.5451.248 · Cited by 4,250
Hollmann, M., & Heinemann, S. F. (1994) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.17.030194.000335 · Cited by 4,056
Mischel, W., & Shoda, Y. (1995) · Psychological Review · doi:10.1037/0033-295x.102.2.246 · Cited by 3,889
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Donchin, E., & Coles, M. G. H. (1988) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00058027 · Cited by 3,585
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Wilson, H. R., & Cowan, J. D. (1972) · Biophysical Journal · doi:10.1016/s0006-3495(72)86068-5 · Cited by 3,579
The Kuramoto model: A simple paradigm for synchronization phenomena
Acebrón, J. A., Bonilla, L. L., Vicente, C. J. P., Ritort, F., & Spigler, R. (2005) · Reviews of Modern Physics · doi:10.1103/revmodphys.77.137 · Cited by 3,579
Dynamic predictions: Oscillations and synchrony in top–down processing
Engel, A. K., Fries, P., & Singer, W. (2001) · Nature Reviews Neuroscience · doi:10.1038/35094565 · Cited by 3,454
(1993)
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Basheer, I. A., & Hajmeer, M. N. (2000) · Journal of Microbiological Methods · doi:10.1016/s0167-7012(00)00201-3 · Cited by 3,316
Olds, J., & Milner, P. (1954) · Journal of Comparative and Physiological Psychology · doi:10.1037/h0058775 · Cited by 3,176
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Singer, W., & Gray, C. M. (1995) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.18.030195.003011 · Cited by 3,217
Being There: Putting Brain, Body and World Together Again.
van Gelder, T., & Clark, A. (1998) · The Philosophical Review · doi:10.2307/2998391 · Cited by 3,134
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Katz, L. C., & Shatz, C. J. (1996) · Science · doi:10.1126/science.274.5290.1133 · Cited by 3,048
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Martin, S. J., Grimwood, P. D., & Morris, R. G. M. (2000) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.23.1.649 · Cited by 2,794
Melzack, R., & Wall, P. D. (1996) · Pain Forum · doi:10.1016/s1082-3174(96)80062-6 · Cited by 2,786
Rasch, B., & Born, J. (2013) · Physiological Reviews · doi:10.1152/physrev.00032.2012 · Cited by 2,783
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Marr, D. (1971) · Philosophical Transactions of the Royal Society of London. B, Biological Sciences · doi:10.1098/rstb.1971.0078 · Cited by 2,771
The psychology of curiosity: A review and reinterpretation.
Loewenstein, G. (1994) · Psychological Bulletin · doi:10.1037/0033-2909.116.1.75 · Cited by 2,738
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Nader, K., Schafe, G. E., & Le Doux, J. E. (2000) · Nature · doi:10.1038/35021052 · Cited by 2,706
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Barnes, C. A. (1979) · Journal of Comparative and Physiological Psychology · doi:10.1037/h0077579 · Cited by 2,088
Bruner, J. S. (1957) · Psychological Review · doi:10.1037/h0043805 · Cited by 2,086
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Baltes, P. B. (1997) · American Psychologist · doi:10.1037/0003-066x.52.4.366 · Cited by 2,066
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Bowlby, J. (1973) · Cited by 2,012
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Grossberg, S. (1976) · Biological Cybernetics · doi:10.1007/bf00344744 · Cited by 1,910
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Xia, Q., & Yang, J. J. (2019) · Nature Materials · doi:10.1038/s41563-019-0291-x · Cited by 1,800
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Tsien, J. Z., Huerta, P. T., & Tonegawa, S. (1996) · Cell · doi:10.1016/s0092-8674(00)81827-9 · Cited by 1,782
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Packard, M. G., & Knowlton, B. J. (2002) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.25.112701.142937 · Cited by 1,717
Two-stage model of memory trace formation: A role for “noisy” brain states
Buzsáki, G. (1989) · Neuroscience · doi:10.1016/0306-4522(89)90423-5 · Cited by 1,709
Softky, W., & Koch, C. (1993) · The Journal of Neuroscience · doi:10.1523/jneurosci.13-01-00334.1993 · Cited by 1,689
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Beale, M. H., Hagan, M., & Demuth, H. B. (2015) · Cited by 1,643
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Grossberg, S. (1988) · Neural Networks · doi:10.1016/0893-6080(88)90021-4 · Cited by 1,635
Bar, M. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1476 · Cited by 1,613
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LeDoux, J. (2012) · Neuron · doi:10.1016/j.neuron.2012.02.004 · Cited by 1,594
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Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., & Tuytelaars, T. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01219-9_9 · Cited by 1,590
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Cahill, L., & McGaugh, J. L. (1998) · Trends in Neurosciences · doi:10.1016/s0166-2236(97)01214-9 · Cited by 1,575
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Dong, S., Wang, P., & Abbas, K. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2021.100379 · Cited by 1,622
Awad, M., & Khanna, R. (2015) · Apress eBooks · doi:10.1007/978-1-4302-5990-9 · Cited by 1,625
Tononi, G. (1998) · Science · doi:10.1126/science.282.5395.1846 · Cited by 1,561
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Cellular and network mechanisms of rhythmic recurrent activity in neocortex
Sanchez-Vives, M. V., & McCormick, D. A. (2000) · Nature Neuroscience · doi:10.1038/79848 · Cited by 1,539
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Kano, M., Ohno‐Shosaku, T., Hashimotodani, Y., Uchigashima, M., & Watanabe, M. (2009) · Physiological Reviews · doi:10.1152/physrev.00019.2008 · Cited by 1,595
Optimal unsupervised learning in a single-layer linear feedforward neural network
Sanger, T. D. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90044-0 · Cited by 1,527
Induction of plasticity in the human motor cortex by paired associative stimulation
Stefan, K. (2000) · Brain · doi:10.1093/brain/123.3.572 · Cited by 1,484
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Anderson, J. R. (2007) · doi:10.1093/acprof:oso/9780195324259.001.0001 · Cited by 1,483
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Bi, G., & Poo, M. (2001) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.24.1.139 · Cited by 1,525
Toward a modern theory of adaptive networks: Expectation and prediction.
Sutton, R. S., & Barto, A. G. (1981) · Psychological Review · doi:10.1037/0033-295x.88.2.135 · Cited by 1,450
Storage of 7 +/- 2 short-term memories in oscillatory subcycles
Lisman, J. E., & Idiart, M. A. P. (1995) · Science · doi:10.1126/science.7878473 · Cited by 1,471
Self-organization in a perceptual network
Linsker, R. (1988) · Computer · doi:10.1109/2.36 · Cited by 1,473
Neuronal signalling of fear memory
Maren, S., & Quirk, G. J. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1535 · Cited by 1,428
Learning, Remembering, and Understanding
Reynolds, S. (2017) · Routledge eBooks · doi:10.4324/9781315211985-5 · Cited by 1,428
Developmental mechanisms that generate precise patterns of neuronal connectivity
Goodman, C. S., & Shatz, C. J. (1993) · Cell · doi:10.1016/s0092-8674(05)80030-3 · Cited by 1,425
Experience and brain development
Greenough, W. T., Black, J. E., & Wallace, C. S. (1987) · Child Development · doi:10.2307/1130197 · Cited by 1,398
Chapter 20 What are the differences between long-term, short-term, and working memory?
Cowan, N. (2008) · Progress in Brain Research · doi:10.1016/s0079-6123(07)00020-9 · Cited by 1,394
Feature Discovery by Competitive Learning*
Rumelhart, D. E., & Zipser, D. (1985) · Cognitive Science · doi:10.1207/s15516709cog0901_5 · Cited by 1,398
The nature and measurement of meaning.
Osgood, C. E. (1952) · Psychological Bulletin · doi:10.1037/h0055737 · Cited by 1,322
The maturing architecture of the brain's default network
Fair, D. A., Cohen, A. L., Dosenbach, N. U. F., Church, J. A., Miezin, F. M., Barch, D. M., Raichle, M. E., Petersen, S. E., & Schlaggar, B. L. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0800376105 · Cited by 1,314
Closed-loop brain training: the science of neurofeedback
Sitaram, R., Ros, T., Stoeckel, L., Haller, S., Scharnowski, F., Lewis-Peacock, J., Weiskopf, N., Blefari, M. L., Rana, M., Oblak, E., et al. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2016.164 · Cited by 1,306
Hippocampal synaptic enhancement and information storage within a distributed memory system
McNaughton, B. L., & Morris, R. G. M. (1987) · Trends in Neurosciences · doi:10.1016/0166-2236(87)90011-7 · Cited by 1,304
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Magee, J. C., & Johnston, D. (1997) · Science · doi:10.1126/science.275.5297.209 · Cited by 1,340
Cognitive Neuroscience and the Study of Memory
Milner, B. A., Squire, L. R., & Kandel, E. R. (1998) · Neuron · doi:10.1016/s0896-6273(00)80987-3 · Cited by 1,335
Metadata
- Authors
- Hebb, D. O.
- Corresponding author
- Hebb, D.O.
- Venue
- Psychology Press eBooks
- Publisher
- Psychology Press
- Published
- 2005-04-11
- Pages
- 43-54
- OpenAlex
- W22297218
- Cited by
- 9,825
- References
- 0
- Citations by year
- 2026: 65 · 2025: 116 · 2024: 113 · 2023: 145 · 2022: 142 · 2021: 217 · 2020: 252 · 2019: 223 · 2018: 244 · 2017: 236 · 2016: 259 · 2015: 283 · 2014: 319 · 2013: 306 · 2012: 258
- FWCI
- 91.14
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Psychology Press eBooks (publishedVersion)
- The MIT Press eBooks
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Neurology › Neurology and Historical Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- synaptic organization, artificial neural networks, Hebbian learning rule, environmental enrichment, experience-dependent brain development, neuropsychological theory, history of neuroscience
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